Artificial Intelligence (AI) in Drug Discovery – Thematic Research

Pages: 73 Published: June 28, 2022 Report Code: GDHCHT333

In recent years, driven by the COVID-19 pandemic, the pharma industry has undergone increased levels of digital transformation. The availability of ultra-large datasets and technological advances has led to more interest in the use of artificial intelligence (AI) and big data analytics across the pharma value chain, from drug discovery and clinical trial design, right through to sales and marketing.

Over the past 3-4 years, there has been increased interest in the use of AI in drug discovery, as witnessed by the emergence of an ever-growing number of start-ups operating in this area, increasing number of drug discovery partnerships, and record levels of investment. While most drugs developed using AI are in the early stages of development, there have been some recent major milestones, including the first drug developed by AI to enter clinical trials and the repurposing of an already marketed drug to treat COVID-19.

The artificial intelligence in drug discovery thematic intelligence report assesses provides an overview of the current landscape, including healthcare, technology, regulatory, and macroeconomic trends, as well as key players, while also highlighting opportunities for the use of AI in the future. Furthermore, it provides an industry-specific analysis based on GlobalData databases and surveys, as well as several case studies.

Key Trends

The key trends that will shape the AI in drug discovery theme can be classified into four categories: healthcare trends, technology trends, macroeconomic trends, and regulatory trends.

  • Healthcare trends – The key healthcare trends that will shape the AI in drug discovery theme are COVID-19 on digital transformation in pharma, the rising cost of R&D and dwindling pipelines, increasing availability of ultra-large datasets, increased need for open science in drug discovery, pharma companies building in-house AI capabilities, formation of industry consortia in AI, and role of AI in precision and personalized medicine.
  • Technology trends – The report focuses on key technology trends impacting AI in drug discovery theme including the role of technology giants in AI-based drug discovery, big data, cloud, quantum computing, and cybersecurity.
  • Macroeconomic trends – The key macroeconomic trends that will shape the AI in drug discovery theme are addressing AI skills shortages, increased AI partnerships in drug discovery, increased volume and value of AI-related funding deals in drug discovery, mergers and acquisitions (M&As), and China’s quest for AI dominance.
  • Regulatory trends – The report highlights the key regulatory trends shaping the AI in drug discovery theme including the International Coalition of Medicines Regulatory Authorities (ICMRA) report on the use of AI to develop drugs, regulatory divergence, the European Commission (EC) white paper, and the proposed framework on AI, and the US leadership in AI. 

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AI in Drug Discovery – Industry Analysis

The pharma industry is slow when adopting new technologies such as AI. However, there has been increased activity over the past few years. Many more companies, including recognized leaders, are adopting AI as part of their digital transformation strategies. AI will be a key driver of healthcare innovation in the future through various applications that include management of chronic diseases, drug discovery and development, improvement of clinical trials, manufacturing, and supply chains. COVID-19 has been one of the reasons for the rapid innovation and investment in AI. Compared to pharma and medical device companies, GlobalData forecasts that healthcare providers will spend the most on AI platforms in 2024.

The AI in drug discovery market analysis also covers:

  • Analysis of drugs discovered using AI
  • Survey data on the adoption of AI in pharma
  • Deals
  • Case studies
  • Hiring trends
  • Company filing trends
  • Social media trends

Global AI Platform Revenue in Pharma, Medical, and Healthcare, 2019-2024

Global AI Platform Revenue in Pharma, Medical, and Healthcare, 2019-2024

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AI in Drug Discovery - Value Chain Analysis

The main applications of AI algorithms in drug discovery are the identification and validation of drug targets, virtual screening of compounds, de novo drug design, drug repurposing, and identification of treatment response biomarkers.

Drug discovery begins with the identification of drug targets, which are molecules that are inherently linked to a particular disease process. They should exhibit several features such as involvement in a crucial biological pathway, functionally and structurally characterized, and the ability to interact with drug-like compounds.

AI in Drug Discovery Value Chain

AI in Drug Discovery Value Chain

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Leading AI Technology Vendors

Some of the leading technology vendors within the AI in drug discovery theme are:

  • Microsoft
  • Alphabet
  • IBM

Leading AI Specialists in Drug Discovery

Some of the leading AI specialists in drug discovery are:

  • AbCellera
  • Atomwise
  • Auransa

Leading Pharma Adopters of AI in Drug Discovery

Some of the leading pharma adopters of AI in drug discovery are:

  • AstraZeneca
  • Bristol Myers Squibb
  • GSK

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AI in Drug Discovery Market Overview

Report Pages 73
Regions Covered Global
Key Trends Healthcare Trends, Technology Trends, Macroeconomic Trends, and Regulatory Trends
Value Chains Target Identification and Validation, Generation of Molecule Leads/De Novo Drug Design, Drug Repurposing, and Response Biomarker Discovery
Leading AI Technology Vendors Microsoft, Alphabet, and IBM among others
Leading Specialist AI Vendors AbCellera, Atomwise, and Auransa among others
Leading Pharma adopters AstraZeneca, Bristol Myers Squibb, and GSK among others

 

Reasons to Buy

  • See who the leading players are in the AI in drug discovery space.
  • See how the competitive landscape is evolving, with a review of company activity including strategic partnerships and funding deals, as well as mergers and acquisitions (M&A).
  • See what trends are driving the use of AI in drug discovery.
  • See an analysis of drugs discovered by AI, including by company, phase of development, therapy area, and molecule type.

Key Players

  • BenevolentAI

  • Exscientia
  • Insilico Medicine
  • Recursion Pharmaceuticals
  • Berg
  • Cyclica
  • Atomwise
  • Lantern Pharma
  • Insitro
  • Standigm
  • Healx
  • Owkin
  • Alphabet
  • Nvidia
  • Tencent
  • International Business Machines (IBM)
  • Microsoft
  • Baidu
  • Alibaba

Table of Contents

Executive Summary

Players

Thematic Briefing

Trends

• Healthcare Trends

• Technology Trends

• Regulatory trends

• Macroeconomic Trends

Industry Analysis

• Market size and growth forecasts

• Analysis of drugs discovered using AI

• Survey data on the adoption of AI in pharma

• Deals

• Case studies

• Hiring trends

• Company filings trends

• Social media trends

Value Chain

• Target identification and validation

• Generation of molecule leads/de novo drug design

• Drug repurposing

• Response biomarker discovery

Companies

• Leading AI technology vendors

• Specialist AI vendors in drug discovery

• Leading pharma adopters of AI in drug discovery

Appendix

• Abbreviations

• Further Reading

• About the Authors

• Our Thematic Research Methodology

• About GlobalData

• Contact Us

List of Tables

Table 1: Healthcare trends impacting AI in drug discovery

Table 2: Technology trends impacting AI in drug discovery

Table 3: Macroeconomic trends impacting AI in drug discovery

Table 4: Regulatory trends impacting AI in drug discovery

Table 5: Examples of drugs in clinical development by highest phase of development

Table 6: Top 20 pharma partnerships in AI-based drug discovery by value

Table 7: Examples of top VC deals associated with AI in drug discovery

Table 8: Examples of M&A deals associated with AI in drug discovery

Table 9: Examples of publicly available platforms and databases used for target identification

Table 10: Examples of AI technologies used for target identification

Table 11: Examples of companies with technology for generation of molecule leads and de novo drug design

List of Figures

Figure 1: Examples of leading players in AI in drug discovery and where do they sit in the value chain?

Figure 2: Key components of machine learning

Figure 3: Global AI platform revenue in pharma, medical, and healthcare, 2019–24

Figure 4: Top companies by number of drugs developed using AI-based technologies

Figure 5: Breakdown of drugs by highest phase of development

Figure 6: Breakdown of drugs by therapy area

Figure 7: Breakdown of drugs by molecule type

Figure 8: Role of AI in optimizing drug discovery and development

Figure 9: Current and expected use of AI in drug discovery and development

Figure 10: Most pharma companies will use AI vendors to implement the technology across their value chain

Figure 11: Impact of the COVID-19 pandemic on investment in AI

Figure 12: Technologies pharma is prioritizing for current investments

Figure 13: Investment in emerging technologies over the next two years

Figure 14: Use of AI in drug discovery and development is expected to peak in more than nine years

Figure 15: Number of AI-based drug discovery strategic alliances has increased since 2015

Figure 16: Top AI vendors by number of deals, 2015–22

Figure 17: Top pharma companies by number of AI drug discovery deals, 2015–22

Figure 18: Number and value of AI-based drug discovery VC deals has increased since 2015

Figure 19: AI job postings in pharma, 2019–22

Figure 20: Number of AI mentions in company filings, 2016–22

Figure 21: Top influencer trends related to AI

Figure 22: Top influencer posts related to AI and drug discovery, 2019–22

Figure 23: AI in drug discovery value chain

Figure 24: Examples of leaders and challengers in target identification and validation

Figure 25: Computer-aided drug discovery methods

Figure 26: Examples of leaders and challengers in molecule lead generation and de novo drug design

Figure 27: Examples of leaders and challengers in drug repurposing

Figure 28: Examples of leaders and challengers in response biomarker discovery

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